information of genomics quantitative lecture outline
Lecture One: Principles of Genetic Linkage and Map Creation
Background – Thomas Hunt and Alfred Sturtevant with the
idea that mutations can be in linkage. Recombination between
traits - crossing over during meiosis leading to disruption
between traits. The use of number of recombinations to
separate traits, distance in centimorgans.
Genetic markers used for analysis, types of markers available.
Crossing between two populations. The idea of a genetic map.
Methods for measuring genetic distances. Use of three (and
more) markers to establish gene order and distance (travelling
salesman problem). Mapping functions (Kosambi, Haldane).
Practical One: Genetic Map construction.
Use R (maybe crimap?) to perform a map construction on
either a simulated or real (but limited) dataset.
Try different methods of map construction. Counting the
number of recombinations.
Lecture Two: Principles of QTL analysis
Introduction to QTL analysis, definition of a QTL. Differences
between Mendelian (simple) and quantitative (complex) traits.
Crossing populations – types of QTL crosses (backcross, F2
intercross, advanced intercross, recombinant inbred lines).
Measuring phenotypes and the regression of phenotypes on
marker genotypes. Placing traits on the genetic map.
Statistical methods for single marker analysis (T test!).
Problems with this approach (close and distant linkage
confounded with large and small effect size).
Interval mapping and its advantages.
Composite Interval Mapping, Multiple Interval Mapping and
beyond. Introduction to these only!
Introduction to eQTL analysis and gene networks.
Advantages and disadvantages of QTL analysis.
Practical Two: QTL analysis in R
Use of R/QTL to test mapping using an experimental dataset.
Types of analysis.
Preparation of a dataset. Plotting phenotypes, checking for
outliers, data cleaning.
Segregation distortion (possibly practical one?).
Single marker analysis using regression.
Interval mapping analysis using Haley-Knott regression and
ML.
LOD scores, plotting results.
Background – Thomas Hunt and Alfred Sturtevant with the
idea that mutations can be in linkage. Recombination between
traits - crossing over during meiosis leading to disruption
between traits. The use of number of recombinations to
separate traits, distance in centimorgans.
Genetic markers used for analysis, types of markers available.
Crossing between two populations. The idea of a genetic map.
Methods for measuring genetic distances. Use of three (and
more) markers to establish gene order and distance (travelling
salesman problem). Mapping functions (Kosambi, Haldane).
Practical One: Genetic Map construction.
Use R (maybe crimap?) to perform a map construction on
either a simulated or real (but limited) dataset.
Try different methods of map construction. Counting the
number of recombinations.
Lecture Two: Principles of QTL analysis
Introduction to QTL analysis, definition of a QTL. Differences
between Mendelian (simple) and quantitative (complex) traits.
Crossing populations – types of QTL crosses (backcross, F2
intercross, advanced intercross, recombinant inbred lines).
Measuring phenotypes and the regression of phenotypes on
marker genotypes. Placing traits on the genetic map.
Statistical methods for single marker analysis (T test!).
Problems with this approach (close and distant linkage
confounded with large and small effect size).
Interval mapping and its advantages.
Composite Interval Mapping, Multiple Interval Mapping and
beyond. Introduction to these only!
Introduction to eQTL analysis and gene networks.
Advantages and disadvantages of QTL analysis.
Practical Two: QTL analysis in R
Use of R/QTL to test mapping using an experimental dataset.
Types of analysis.
Preparation of a dataset. Plotting phenotypes, checking for
outliers, data cleaning.
Segregation distortion (possibly practical one?).
Single marker analysis using regression.
Interval mapping analysis using Haley-Knott regression and
ML.
LOD scores, plotting results.